A Single-Channel Noise Reduction Filtering/Smoothing Technique in the Time Domain
Bibliographic record
Abstract
In this paper, we present a single-channel smoothing-and-filtering technique for noise reduction in the time domain. Unlike traditional noise reduction methods, which directly apply a noise reduction filter to the noisy signal, the developed technique achieves noise reduction in two steps. It first applies a time smoothing window to the noisy signal, which, on the one hand, can help reduce high frequency noise and, on the other hand, can help leverage the correlation between successive signal samples. A noise reduction filter is then applied to the smoothed noisy signal to estimate the speech signal of interest. Three optimal and suboptimal noise reduction filters are derived, including the Wiener, maximum signal-to-noise-ratio (SNR), and tradeoff filters. Simulation results reveal that the developed method can produce better noise reduction performance, i.e., higher gains in the perceptual-evaluation-of-speech-quality (PESQ) score, than the traditional methods without smoothing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".